Significance-Based Estimation-of-Distribution Algorithms

Estimation of Distribution Algorithms

Parallel estimation of distribution algorithms

The reduction in the execution time is a factor that becomes very important when using many applications nowadays. In spite of the enhancement on computer hardware, this problem is always happening, as the increase in power is always related to the application of new methods that were also too time consuming in previous computer systems. Parallel programming techniques provide feasibility of so...

Parallel Estimation of Distribution Algorithms

The thesis deals with the new evolutionary paradigm based on the concept of Estimation of Distribution Algorithms (EDAs) that use probabilistic model of promising solutions found so far to obtain new candidate solutions of optimized problem. There are six primary goals of this thesis: 1. Suggestion of a new formal description of EDA algorithm. This high level concept can be used to compare the ...

Vine Estimation of Distribution Algorithms

A Vine Estimation of Distribution Algorithm (VEDA) is a recently proposed optimization procedure built on top of a probabilistic graphical model called vine. The rst target of vines was uncertainty analysis with high dimensional dependence modeling. The aim of this communication is to draw a path through a simple set of experiments, from the Univariate Marginal Distribution Algorithm to VEDA. F...

A Survey of Estimation of Distribution Algorithms Based on Copulas

The use of probabilistic models based on copulas in Estimation of Distribution Algorithms (EDAs) has been identi ed as an emerging research trend on these algorithms for continuous domains. By using copulas, the e ect of the dependence structure and the margins in a joint distribution can be represented separately. Consequently, EDAs based on copulas inherit these characteristics and are able t...